16 citations · 36 across the 14 of their papers we have counts for
14 papers
Difference-Driven Gating: Adaptive Feature Fusion for U-Net Decoder
Kai Li, Xuechao Zou, Jiashen Fu +3
The U-Net style models have been widely used in many applications. A critical step in these models is to reconstruct the lower-level features using a top-down decoder. This reconst…
TDFNet: An Efficient Audio-Visual Speech Separation Model with Top-down Fusion
Samuel Pegg, Kai Li, Xiaolin Hu
Audio-visual speech separation has gained significant traction in recent years due to its potential applications in various fields such as speech recognition, diarization, scene an…
RTFS-Net: Recurrent Time-Frequency Modelling for Efficient Audio-Visual Speech Separation
Samuel Pegg, Kai Li, Xiaolin Hu
Audio-visual speech separation methods aim to integrate different modalities to generate high-quality separated speech, thereby enhancing the performance of downstream tasks such a…
IIANet: An Intra- and Inter-Modality Attention Network for Audio-Visual Speech Separation
Kai Li, Runxuan Yang, Fuchun Sun +1
Recent research has made significant progress in designing fusion modules for audio-visual speech separation. However, they predominantly focus on multi-modal fusion at a single te…
Audio-Visual Speech Separation in Noisy Environments with a Lightweight Iterative Model
Héctor Martel, Julius Richter, Kai Li +2
We propose Audio-Visual Lightweight ITerative model (AVLIT), an effective and lightweight neural network that uses Progressive Learning (PL) to perform audio-visual speech separati…
MIXPGD: Hybrid Adversarial Training for Speech Recognition Systems
Aminul Huq, Weiyi Zhang, Xiaolin Hu
Automatic speech recognition (ASR) systems based on deep neural networks are weak against adversarial perturbations. We propose mixPGD adversarial training method to improve the ro…
NP-Match: Towards a New Probabilistic Model for Semi-Supervised Learning
Jianfeng Wang, Xiaolin Hu, Thomas Lukasiewicz
Semi-supervised learning (SSL) has been widely explored in recent years, and it is an effective way of leveraging unlabeled data to reduce the reliance on labeled data. In this wor…
Extracting Semantic Knowledge from GANs with Unsupervised Learning
Jianjin Xu, Zhaoxiang Zhang, Xiaolin Hu
Recently, unsupervised learning has made impressive progress on various tasks. Despite the dominance of discriminative models, increasing attention is drawn to representations lear…
Drug repositioning for Alzheimer's disease with transfer learning
Yetao Wu, Han Liu, Jie Yan +1
Deep Learning and DRUG-seq (Digital RNA with perturbation of genes) have attracted attention in drug discovery. However, the public DRUG-seq dataset is too small to be used for dir…
An efficient encoder-decoder architecture with top-down attention for speech separation
Kai Li, Runxuan Yang, Xiaolin Hu
Deep neural networks have shown excellent prospects in speech separation tasks. However, obtaining good results while keeping a low model complexity remains challenging in real-wor…
NP-Match: When Neural Processes meet Semi-Supervised Learning
Jianfeng Wang, Thomas Lukasiewicz, Daniela Massiceti +3
Semi-supervised learning (SSL) has been widely explored in recent years, and it is an effective way of leveraging unlabeled data to reduce the reliance on labeled data. In this wor…
Visual Recognition by Request
Chufeng Tang, Lingxi Xie, Xiaopeng Zhang +2
Humans have the ability of recognizing visual semantics in an unlimited granularity, but existing visual recognition algorithms cannot achieve this goal. In this paper, we establis…
On the Use of Deep Mask Estimation Module for Neural Source Separation Systems
Kai Li, Xiaolin Hu, Yi Luo
Most of the recent neural source separation systems rely on a masking-based pipeline where a set of multiplicative masks are estimated from and applied to a signal representation o…
Infrared Invisible Clothing:Hiding from Infrared Detectors at Multiple Angles in Real World
Xiaopei Zhu, Zhanhao Hu, Siyuan Huang +2
Thermal infrared imaging is widely used in body temperature measurement, security monitoring, and so on, but its safety research attracted attention only in recent years. We propos…